This proposal describes the core technology that is relevant to both rigid and non-rigid registration applications. We will use these registration methods for the purposes of image fusion (i.e. merging of multiple diagnostic imaging acquisitions of the same patient), as well as for template- driven segmentation TDS) (i.e. algorithms used to warp atlas data sets into the configuration of a new MR data set). In our proposal, the clinical significance this technology is demonstrated for surgical planning and visualization as well as for intraoperative image-guidance. Our prior related research on registration and deformable modeling has concentrated on manual information (MI)-based rigid registration and non-rigid registration for template-driven segmentation (TDS). Further improvement and development is necessary on engineering features of our existing rigid register methods, and to implement the non rigid registration for surgical applications and for template driven segmentation. One of our goals is to enhance the exploitation of the anatomic and functional information available in medical imagery for use in image-guided therapy. We will provide the surgeon with access to this information, registered across modalities and (in the case of procedures in the interventional or intraoperative MRI unit) registered to the anatomy of the patient. This information may enable the surgeon to more precisely identify and avoid critical structures and to more accurately locate pathological tissues. In the area of registration we will continue to develop clinically relevant registration methods and elastic matching algorithms. These algorithms are used both for image fusion, i.e. merging of multiple diagnostic imaging acquisitions of the same patient, and as part of template-driven segmentation algorithms that warp atlas data sets into the configuration of a patient's brain.